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genpark-hidden-markov-model-viterbi-decoder-skill

mcp
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SUMMARY

GenPark AI Agent Skill - Hidden Markov Model (HMM) Viterbi dynamic programming decoder determining the most probable sequence of hidden states from noisy observation streams.

README.md

GenPark Hidden Markov Model Viterbi Decoder Skill

Viterbi dynamic programming algorithm for decoding maximum a posteriori (MAP) state sequences in Hidden Markov Models.

Check out GenPark and the GenPark MCP Catalog.

graph LR
    subgraph t=0
        H0[HEALTHY]
        F0[FEVER]
    end
    subgraph t=1
        H1[HEALTHY]
        F1[FEVER]
    end
    subgraph t=2
        H2[HEALTHY]
        F2[FEVER]
    end
    H0 -->|Viterbi Trellis| H1
    H1 -->|Viterbi Trellis| F2
    style H0 fill:#e8f5e9
    style H1 fill:#e8f5e9
    style F2 fill:#ffebee

Features

  • Dynamic programming log-space calculations avoiding underflow.
  • Exact backpointer sequence path reconstruction.
  • Pure Python standard library.

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